For the purpose of quantitative analysis in near-infrared spectroscopy, multivariate methods such as principal component regression analysis and PLS regression analysis are frequently used. These methods are for performing multivariate analyses of spectral data obtained through experiments and the like, and a target component is quantified using a calibration curve (calibration model) obtained from experimental data.
In particular, for the purpose of a quantitative and qualitative analysis in a mid-infrared region, a method called a curve fitting method with which a composite spectrum including a large number of peaks resulting from light absorption characteristics of a plurality of components is divided into component spectra of the components is known as another conventional method.
The curve fitting method is frequently used in the case where the absorption peaks of the components at specific absorption wavelengths are sharp and clear as in a mid-infrared spectrum. However, in the case of the near-infrared spectrum, there have been few examples in which the curve fitting method is used other than the method for measuring a hemoglobin concentration and there are no precedents in which the curve fitting method has been used to quantify a glucose concentration.
Also, a method called CLS (classical least squares) is known as a similar conventional method with which a spectrum is synthesized using component spectra.
The CLS method is also frequently used in a spectroscopic analysis in a mid-infrared region and is for synthesizing a measurement spectrum using component spectra as parameters. The CLS method is a method to which the Lambert-Beer law, which states that absorbance is in proportion to a concentration and a length of a light path, is applied to a multi-component (multi-factor) component analysis as it is. There are also no precedents in which this method has been used to quantify a glucose concentration.
The following are the advantages of the CLS method:                It is important to estimate the number of components to be used in synthesis of a spectrum, and when the estimation is accurate, accurate quantification can be performed.        Since the spectra of biological components are used, the meanings of parameters are clear.        
In contrast, the following are said to be the drawbacks:                If the estimation of unexpected disturbance factors, particularly the number of components, has been misread, the quantification accuracy will decrease.        The above-mentioned number of components includes the number of unpredictable components, device errors, and the like.        
The reason why the above-mentioned two conventional methods are not frequently used in the near-infrared spectroscopy is that in the near-infrared spectrum, the component spectra have a broad shape and do not include clear absorption peaks, and that when a minor component such as a glucose component in a living organism is analyzed, a spectral change in the minor component is smaller than spectral changes in other components, and thus it is difficult to apply the spectrum synthesis methods such as the curve fitting method and the CLS method.